Türk Tarım ve Doğa Bilimleri Dergisi 1(3): 301–311, 2014
TÜRK
TARIM ve DOĞA BİLİMLERİ
DERGİSİ
TURKISH
JOURNAL of AGRICULTURAL
and NATURAL SCIENCES
www.turkjans.com
Genetic Parameters Estimation for Some Functional Milk Traits of Brown Swiss Dairy Cattle
a
a
Kemal YAZGAN* , bJale METIN KIYICI
Harran University, Faculty of Agriculture, Department of Animal Science, Sanliurfa - TURKEY
b
Faculty of Agriculture, Department of Animal Science, Kayseri - TURKEY
*Corresponding author: [email protected]
Received: 31.12.2013 Received in Revised Form: 27.4.2014
Accepted: 28.04.2014
Abstract
The objective of this study was to estimate heritabilities and to investigate the genetic relationship
between some functional milk traits such as milk yield (MY), dry matter (DM), fat (F) and milking duration (MD)
on Brown Swiss dairy cattle breeding in Turkey with random regression model using heterogeneous residual
error variance interval. Variance components were estimated with 4 multiple-trait (4 traits at a time) random
regression model via restricted maximum likelihood (REML) with AI-REML algorithm. Data were obtained from
an experimental farm and comprised 636 test day (TD) records for each trait. Average heritability for MY, DM, F
and MD were 0.29, 0.10, 0.16 and 0.05, respectively. The largest genetic correlation interval were found
between F and MD and ranged from -0.34 to 0.72 throughout lactation. The shape of genetic correlation curve
of DM-MD was similar to F-MD and genetic correlation between DM-MD ranged from -0.24 to 0.61. Genetic
correlations of other traits changed from 0.08 to 0.65 for MY-DM, -0.16 to 0.28 for MY-MD and 0.04 to 0.59 for
MY-F throughout lactation. Results from this study implied that increasing fat percentage in milk and other milk
components may have led to decrease the flow of milk and consequently, affected the milking duration.
Key words: Random regression model, functional milk traits, genetic parameter
Esmer Irkı Süt Sığırlarında Bazı İşlevsel Süt Verim Özelliklerine İlişkin Genetik Parametre
Tahminleri
Özet
Bu çalışmanın amacı Türkiye de yetiştiriciliği yapılan Esmer ırkı süt sığırlarında süt verimi (MY), kuru
madde (DM), süt yağı (F) ve sağım süresi (MD) gibi bazı işlevsel süt verim özelliklerinin kalıtım derecelerini
hesaplamak ve bu özelliklerin aralarındaki genetik ilişkiyi araştırmaktır. Araştırma da şansa bağlı regresyon
modeli kullanılmış ve denetim günleri aralıklarında hata varyansının farklı olduğu kabul edilmiştir. Varyans
bileşenleri şansa bağlı regresyon modelinde dört özelliğin aynı anda modele dahil edilmesi ile kısıtlanmış en
yüksek olabilirlik (REML) metodu ile AI-REML algoritması kullanarak tahminlenmiştir. Araştırma da deneysel bir
çiftlikten elde edilen ve her bir süt verim özelliğinin dahil olduğu 636 denetim günü kaydı kullanılmıştır. MY,
DM, F and MD için kalıtım dereceleri ortalamaları sırasıyla 0.29, 0.10, 0.16 ve 0.05 olarak hesaplanmıştır.
Özellikler içinde laktasyon boyunca en geniş genetik korelasyon aralığı F - MD arasında saptanmış olup -0.34 ile
0.72 aralığında değişmiştir. DM-MD arasındaki genetik korelasyon eğrisinin şekli F-MD arasındaki genetik
korelasyon eğrisi ile benzerlik göstermiş ve değerler -0.24 ile 0.61 aralığında saptanmıştır. laktasyon boyunca
diğer özelliklere ilişkin genetik korelasyonlar MY-DM için 0.08 ile 0.65, MY-MD için -0.16 ile 0.28 ve MY-F için
ise 0.04 ile 0.59 aralığında değişmiştir. Bu sonuçlar süt bileşenleri içinde yağ yüzdesindeki artışın süt akıcılığını
azaltarak süt sağım hızını etkilediğini göstermektedir.
Anahtar kelimeler: Şansa bağlı regresyon modeli, işlevsel süt verim özellikleri, genetik parametre
301
Türk Tarım ve Doğa Bilimleri Dergisi 1(3): 301–311, 2014
Introduction
Literature regarding genetic parameters
especially genetic correlations between milk
production traits such as fat, protein, lactose and
milk urea nitrogen for dairy cattle populations
breeding in Turkey is very scarce. In dairy cattle,
random regression model methodology is
commonly used for regular genetic evaluation of
milk production traits (Interbull, 2007; Bohmanova
et al., 2008), such as F, protein and lactose (i.e.).
Considering literature for genetic parameters for
dry matter is fairly rare. Yield of DM components
(fat protein, lactose) is advantageous for the needs
of milk processing and could be the reason for an
increasing interest in the analysis of DM in milk.
Considering the chemical analysis, dry matter is
relatively easy to carry out, it seems to be
worthwhile to estimate genetic parameters for
that trait and to use the results in breeding
practice (Yazgan et al., 2010).
Milking speed is a functional trait that
relates to the incidence of clinical mastitis, labor
time, and electrical power (Boettcher et al., 1998;
Ilahi and Kadarmideen, 2004; Karacaören et al.,
2006). And also, long milking duration results with
more electricity consumption. A single milking
speed observation per animal may be insufficient,
because genetic and environmental factors that
affect the milking speed of individual cows may
vary during a lactation or between subsequent
lactations and milking duration may have optimum
because most producers prefer cows with
relatively uniform milking duration that do not
decrease the flow of cows through the milking
parlor (Zwald et al., 2005).
When aimed to estimating (co)variance
component, to reduce the number of parameters
and the dimension of likelihood searchers and
some computing problems, error variances may be
assumed constant throughout lactation. In practice
error variance can be highly variable not only
monthly interval but also day to day. So, when use
random regression model in genetic analysis if
defining classes of residual variances by test days
records, estimation of genetic parameters can be
more accurate. When the models applied in pairwise analyses obtaining genetic parameters such as
heritability and genetic correlation could not be
same as those fitted for each of the traits in the
univariate analyses. Furthermore, considering a
trait, could be different for this trait between two
pair-wise analyses. If additive genetic correlation is
low between traits, order of estimated breeding
values can be different by univariate and
multivariate analyses (Kumlu, 2003). Using with
four traits at a time, obtained genetic parameters
can be more accurate. Especially, more than two
traits with large data set, multivariate random
regression analyses needs to large computer
memory (RAM) and processor (CPU). However,
owing to development of computer and software
technology, computing problems such as long CPU
time, has begun to decrease and made possible the
multivariate (four or more trait) analyses.
Various REML algorithms were developed to
compute variance and covariance components.
The relatively simpler algorithm is derivative-free
methods. Because of no derivate are used in this
method and potentially unreliable for complicated
model such as multiple traits. On the other hand
average information (AI-REML) algorithm is the
most popular algorithm involving the second
derivate (Misztal ,2008).
In Turkey there are only very few studies on
genetic correlation between milk yield, fat and
milking duration traits that we chose to analyze in
this research. Especially fat ratio in milk may be an
important factor affecting milk flow rate or milking
duration. Therefore, there is a need to research
and detect the correlations with other traits.
Additionally, obtained parameters via such
calculations i.e. breeding values and other genetic
parameters can be used for selection of animals.
Therefore objective of this study was to
estimate heritabilities and genetic relationships
between some functional milk traits such as milk
yield (MY), milk dry matter (DM), milk fat (F) and
milking duration (MD) on Brown Swiss dairy cattle
breeding in Turkey with random regression model
using heterogeneous residual error variance
interval.
Materials and Methods
Data
Data set were the first, second and third
lactation records of 59 healthy Brown Swiss dairy
cows raised at experimental research farm of the
Atatürk University, Agriculture Faculty in Erzurum
province of Turkey over the period from 2006 to
2007. Traits (MY, DM, F and MD) were recorded
fortnightly. Because animals used for another
research, MY were recorded until 196th days in
milk (DIM). MY and MD were measured twice
(morning and evening) with milk-o meter unit that
have the traditional type (Sharaby et al., 1977). A
milk-o tester device was used to analyses for F and
for the analysis of milk dry matter was used drying
method in oven. The animals sheltered in
compartments having open and closed section.
Dimensions of the panes were; 25.0m x 8.0m for
the open pail and 5.0m x 9.50m x 8.0m for the
closed pail. Roughage was supplied in the open
division at the feeders which has of 1.0 m width
and 8.0 m length. In the research is not
302
Türk Tarım ve Doğa Bilimleri Dergisi 1(3): 301–311, 2014
implementing a special feed or feeding program.
Cows in the barn were supplied with ad-libitum
roughage twice a day (09:00 pm and 14:00 am).
The cows had no limitation for water supply.
According to actual milk yield, the concentrate
feed was supplied during the milking for each cow,
separately. Components of the concentrate feed
consisted of 18% crude protein, 2.80% crude fat,
crude fiber 9.90%, crude ash 8.40%, macro
elements, micro elements, vitamins. Concentrate
feed that had 2700 mcal/ kg energy. Total number
of animals in pedigree file was 115 (59 cows, 15
sires and 41 dam). There were 13 cows with
unknown sire and 10 cows with unknown dam.
:
is the nth random regression
coefficients for the additive genetic
effect of cow m separate for trait i.
is the nth random regression
coefficient for the permanent
environmental effect of cow m for
trait i.
is a vector of covariates of size 3
(because of second order Legendre
polynomial has 3 parameters)
describing the shape of lactation
curve of fixed and random
regressions evaluated at t DIM.
:
:
: is the residual (different value in each
DIM interval for different traits).
There were 2 calving years (2006-2007) and
3 class of lactation parity (1-3). In matrix notation
the model can be written as:
y = Xb + Za + Wp + e
Where; y is a vector of observations, b is a vector
of fixed effects, a and p are vectors of random
regression coefficients for additive genetic and
permanent environmental effects, respectively, e is
a vector of residuals. Matrices X, Z and W are
incidence matrices which relate observations to
effects. The (co)variance matrix for random effects
in the model can be written as:
Figure 1. Lactation curve fitted by second order
Legendre polnomiyal
Statistical analysis and models
Variance components, covariances and
genetic parameters for MY, MD, F and MD were
estimated with 4 multiple-trait (4 traits at a time)
random regression model via restricted maximum
likelihood (REML) with AI-REML algorithm, using
WOMBAT software (Meyer, 2007). The multiple
traits random regression model for the genetic
analysis can be written as:
Where G and P are (co)variance matrices for
additive genetic and permanent environmental
random regression coefficients, respectively. A is
additive genetic relationship matrix, I is the
identity
:
:
:
and
R=I σ e2 .
Residuals
variances σ e2 ,
were assumed as heterogeneous
between DIM intervals for different traits. DIM
intervals were arranged as 1-16, 17-31, 32-46, 4761, 62-76, 77-91, 92-106, 107-121, 122-136, 137151, 152-166, 167-182 and 183-196. To obtain
covariance and full genetic correlations between
all traits throughout DIM, following expression was
used;
Where;
:
matrix,
is the oth test-day record of the mth
cow for a trait i (TD milk, milk dry
matter, fat percentage or milking
time).
is the jth year effect for a trait i.
is the kth parity effect for a trait i.
is the nth fixed regression coefficient
for a trait i and specific to the lth TD
class.
where
(56 56) is the matrix includes all
(co)variance at tth DIM for all traits. The matrix G
(12 12) of order k contains the variance
components for the random regression coefficients
in the model. The matrix of order t k contains
second order Legendre polynomial coefficients.
Genetic correlations between all traits at the tth
time were calculated using following expression;
303
Türk Tarım ve Doğa Bilimleri Dergisi 1(3): 301–311, 2014
Figure 2. Residual variance by DIM interval for milk yield (MY), dry matter (DM), fat (F) and milking duration
(MD)
Variances
Residual variances for MY, DM, F and MD by
interval were given in Figure 2. Considering all
traits, fat was the lowest residual error variances
for all intervals and ranged from 0.082 – 1.007. At
Where
: Genetic correlations between two
the 137-151 DIM interval, residual variance of DM
traits at tth DIM time,
:
reached to 6.737 and it was the highest value
covariance between two traits and ,
and
between all traits and all intervals. Residual error
are the respective variances for the two
variance for MY fallowed this and reached 5.080 at
th
traits at t DIM.
the 32-46 DIM interval. Except from F, residual
error variances of traits were undulating form
Results
throughout DIM.
Descriptive statistics for MY, DM, F and MD
Estimates of genetic and permanent
were given in Table 1. The means of MY, DM, F and
environment variances for MY, DM, F and MD
MD were 10.62 kg, 11.22%, 3.90% and 6.70 min
were given in Figure 3. Genetic and permanent
respectively. At result of the analysis Log L was
environmental variances of MY were the highest in
detected as -1680. Lactation curve consisting with
all traits and ranged from 0.32 - 7.68 and 2.13-3.22
estimation daily milk yields was given in Figure 1.
respectively. Alteration of DM and F variance
Considering the shape of the curve it could be said
components were similar. Except MY, especially
that second order Legendre polynomial function
genetic variance have typical ‘’U’’ form for all
defined the lactation curve as typical (Macciotta et
traits. However, for MY, genetic variance values
al., 2005).
were continuously decreasing form. On the other
hand, permanent environmental variances of MD
decreased to 0.05 from 0.37 over 1-136 DIM.
304
Türk Tarım ve Doğa Bilimleri Dergisi 1(3): 301–311, 2014
0.6
Heritability
0.5
DM
0.4
0.3
0.2
0.1
1
16
31
46
61
76
91
106
121
136
151
166
181
196
0
DIM
Figure
Figure 4. Heritability estimations for milk yield
(MY), dry matter (DM), fat (F) and
milking duration (MD)
3. Additive genetic and permanent
environment variances for milk yield
(MY), dry matter (DM), fat (F) and
milking duration (MD)
305
Türk Tarım ve Doğa Bilimleri Dergisi 1(3): 301–311, 2014
Figure 5. Genetic covariances between milk yield - dry matter (MY-DM), milk yield – fat (MY-F), milk yield –
milking duration (MY-MD), dry matter – fat (DM – F), dry matter – milking duration (DM – MD) and fat
– milking duration (F – MD)
After this point, it accented sharply and reached to
0.69 at DIM 196. In addition, after 76 DIM, genetic
variances were less than permanent environmental
variances for MY. Similarly, genetic variances were
less than permanent environmental variances
before 151 DIM for DM. Considering F, between
61-151 DIM, and for MD between 1-121 and 166-
196 DIM, genetic variances were less than
permanent environmental variances.
Heritabilities
Heritability estimations of all traits over DIM
were given in Figure 4 and calculated average
heritabilities from this by traits were given in Table
2. The highest values of heritability (0.56) were
306
Türk Tarım ve Doğa Bilimleri Dergisi 1(3): 301–311, 2014
MY-DM
MY-F
MY-MD
DM-F
DM-MD
F-MD
1
0,8
Genetic correlation
0,6
0,4
0,2
0
-0,2
-0,4
-0,6
-0,8
-1
1
16
31
46
61
76
91
106
DIM
121
136
151
166
181
196
Figure 6. Genetic correlations between milk yield - dry matter (MY-DM), milk yield – fat (MY-F), milk yield –
milking duration (MY-MD), dry matter – fat (DM – F), dry matter – milking duration (DM – MD) and
fat – milking duration (F – MD)
61 and 151DIM. After this point heritability values
found for MY at the beginning of lactation. After
upraised sharply and reached to 0.33 at 196 DIM.
this point, values decreased throughout DIM. At
Considering F, heritability values were in
181 DIM found lowest heritability value for MY and
fluctuating form and they ranged from 0.042 to
it was 0.10. Considering all traits, the lowest
0.33.
heritability values were found for MD during 1- 196
DIM and vary from 0.023 to 0.115. For DM,
Covariances and genetic correlations
heritability values were 0.20 at the beginning of
Genetic correlations and covariances
lactation, however, very low values found between
between MY-DM, MY-F, MY-MD, DM – F, DM – MD
and F – MD were given in Figure 5 and 6.
Table 1. Number of records (n), means ( ), and standard errors (SE) for milk yield (MY), dry matter (DM), fat (F)
and milking duration (MD) by test day (TD) records.
MY (kg)
DM (%)
SE
Fat (%)
SE
MD (min)
SE
SE
TD
DIM1
n
1
1
59
11.82
0.532
10.27
0.244
3.98
0.107
6.98
0.222
2
16
59
12.15
0.557
10.56
0.137
3.58
0.077
7.08
0.244
3
31
59
12.76
0.502
11.32
0.158
3.69
0.066
6.96
0.290
4
46
59
13.04
0.446
11.14
0.142
3.78
0.130
6.73
0.255
5
61
56
11.86
0.375
10.59
0.173
3.64
0.069
6.72
0.281
6
76
54
10.77
0.437
11.30
0.145
3.87
0.091
6.96
0.290
7
91
49
10.83
0.434
11.35
0.124
3.69
0.064
6.46
0.252
8
106
44
10.21
0.471
10.59
0.203
3.79
0.078
6.78
0.319
9
121
41
9.34
0.421
11.76
0.127
3.86
0.059
7.32
0.284
10
136
35
9.14
0.408
10.91
0.213
4.03
0.091
6.17
0.216
11
151
33
8.04
0.405
12.71
0.242
4.84
0.156
6.15
0.327
12
166
31
7.96
0.362
12.41
0.114
4.10
0.080
4.78
0.196
13
181
30
7.28
0.334
12.28
0.184
4.18
0.070
6.99
0.284
196
27
6.11
0.267
11.90
0.239
4.70
0.092
6.86
0.327
14
1Days
in milk
307
Türk Tarım ve Doğa Bilimleri Dergisi 1(3): 301–311, 2014
Throughout
DIM,
highest
genetic
correlations were found between DM – F and
ranged from 0.65- 089. At the middle of the TD
periods covariance between DM and F showed a
downward trend. However, at the beginning of
lactation and towards the end of TD covariance
between DM and F were tending to up rise, as
shown in Figure 6. Although, covariance between
especially MY-F, DM-F and DM-MD have smooth
surface, MY-MD covariance surface was in
fluctuating form (Figure 5). The largest correlation
interval were found for F-MD and ranged from 0.34 (middle of the DIM) to 0.72 (end of DIM). The
shape of genetic correlation curve of DM-MD was
similar to F-MD and genetic correlation for DM-MD
ranged from -0.24 to 0.61. Genetic correlations of
other traits were varying from 0.08 to 0.65 for MYDM, -0.16 to 0.28 for MY-MD and 0.04 to 0.59 for
MY-F. As in shown in Figure 6, except F-MD,
genetic correlations were positive between all
traits, generally.
beginning of lactation and low towards to end of
DIM. Inother words, curve of variances was in
decreasing shape and not typical ‘‘U’’ form as
mentioned earlier. Similar results were obtained
from studies used second order Legendre
polynomial by Takma and Akbaş (2009). Different
from this study, Cobuci et al. (2005), Miglior et al.
(2007) and Galiç and Kumlu (2012) were found
high additive genetic variance at the beginning and
end of lactation when compared with the middle
of lactation. Also, estimated additive genetic
variances for MY in this study were high when
compared the result of research conducted by
Hammami et al. (2008). Estimated additive genetic
variances values for DM throughout DIM were very
close to results from previous study (Yazgan et al.,
2010). Additionally, estimated additive genetic
variances for MD in this study were low when
compared result of research conducted by Zwald
et al. (2005). It could be explained by the fact that
they used different methods of measuring traits
and they analyzed much larger population with
Gibbs sampling.
The
estimation
of
permanent
environmental variance for MY were close to
stable and about 3kg2 throughout DIM (Figure 3).
Different from this study Takma and Akbaş (2009)
reported that permanent environmental variances
for test day milk yields were higher (near to 9 kg 2)
at early lactation and lower (near to 3 kg 2) for the
rest of lactation. In addition, permanent
environmental variances for MY were estimated
over 25 kg2 by Hammami et al. (2008) and very
high when compared with our study. Using
different number of animals and breeds and also
experimental data set used in this research, may
cause
to
obtain
different
permanent
environmental variances.
Discussion
Variances
In this research the estimates residual error
variances for MY were generally lower when
compared to the results obtained in previous
researches (e.g. Karacaören et al., 2006 and
Bohmanova et al., 2008) that error variance were
assumed to be heterogeneous during lactation.
Similarly, residual error variances obtained from
this study for MD throughout DIM (Figure 1), were
generally lower when compared with findings
reported by Karacaören et al. (2006) for milking
speed trait. On the other hand residual error
variances for MD were higher when compared
with findings reported by Zwald et al. (2005). They
calculated residual error variance for MD as 1 min2.
Considering error variance for DM very low
when compared results with previous study by
Yazgan et al. (2010). As in shown Figure 2, except F,
residual error variances were in fluctuating form
for all traits. Unknown environmental factors could
have caused these fluctuations throughout DIM. As
shown in Figure 2, residual error variances for MY
reached to maximum value during at 32-46 DIM.
As shown in Figure 1, most of dairy cows reaches
to the peak yield at this time interval (Macciotta et
al., 2011) and coincide with estrous cycles. This can
explain that why error variance reached to peak
level in this interval. Similarly, DM ratio in milk
begins to increase at the end of lactation when
compared with middle of lactation and this might
cause to increase of error variance for DM during
at 137-151 DIM (Figure 2).
As given in Figure 3, estimated additive
genetic variances for MY were high at the
Heritabilities
In this research estimates of heritability
values for MY were in continuously decreasing
form throughout DIM. It could be explained by;
while permanent environmental variances were
relatively stable throughout DIM, additive genetic
and residual variances were continuously
decreasing parallel to each other. For this data set
were small and obtained from a non-commercial
experimental farm, permanent environmental
variances for MY can be stable. Results from our
research were opposite to the findings reported by
Takma and Akbaş (2009). They found low
heritability value at the beginning of lactation
relative to end of lactation. It could be explained
by the fact that they assumed to be constant
residual error throughout lactation. Whereas in
this research were heterogeneous throughout
308
Türk Tarım ve Doğa Bilimleri Dergisi 1(3): 301–311, 2014
DIM. Also they analyzed a much larger population
of cows and used DF-REML. These could be other
reason for different results between two
researches.
In this research, estimates of heritability for
MY generally were similar when compared to
estimates of Hossein-Zadeh and Ardalan (2011)
and Karacaören et al. (2006). On the other hand,
heritability estimates were lower than the values
obtained from studies conducted by Stoop et al.
(2007), Miglior et al. (2007), Bohmanova et al.
(2008). Conversely, our values were higher than
reported by Haile-Mariam, et al. (2001), Silvestre,
et al. (2005), Hammami, et al. (2008), Yazgan et al.
(2010) and Galiç and Kumlu (2012).
Results from this study for average MY
heritability values were same when compered
another researches which findings obtained by
REML - BLUP procedures but nonrandom
regression methods fulfilled by Ünalan and Cebeci
(2004) and Duru, et al. (2012). However, Ertuğrul,
et. al. (2002) and Tilki, et. al. (2008) were
estimated lower heritability values for MY.
Estimates of heritability values for DM were
slightly lower, when compared to values obtained
from previous research (Yazgan et al., 2010).
However, trend of values throughout DIM were
similar between two researches. While heritability
values at the beginning and end of lactation high,
at the middle of lactation were low for this two
researches. In this research, the highest heritability
values for milk yield while the near to lowest for
DM yield.
Similar with DM, in this research, the
highest heritability values for MY while the lowest
for F yield. Similar results obtained from previous
study by Yazgan et al. (2010). On the other hand
estimates of heritability values for F generally were
similar with the findings from studies conducted by
Hammami et al. (2008) and Yazgan et al. (2010).
However, heritability values for F were lower than
the findings obtained from studies conducted by
Silvestre et al. (2005), Stoop et al. (2007), Miglior
et al. (2007), Bohmanova et al. (2008) and HosseinZadeh and Ardalan (2011).
Considering MD, estimates of heritability
values were very low than results reported by
Zwald et al. (2005). They reported the heritability
value for MD as 0.17. These differences could be
explained by the fact that they used a different
model and method (Sire model and Bayesian
method) of data analyzing and they analyzed a
much larger population of cows. On the other hand
heritability values for MD obtained from our study
were high when compared to heritability values for
milking speed reported by Karacaören et al. (2006).
Table 2. Average heritability for milk yield (MY),
dry matter (DM), fat (F) and milking
duration (MD).
Trait
h2
MY (kg)
0.29
DM (%)
0.10
F (%)
0.16
MD (min)
0.05
Genetic relation between traits
In this research genetic (co)variances and
correlations between traits were estimated using
multiple trait (4 traits at a time) random regression
model. Hence, results can be useful to change
genetic patterns through selection using multiple
trait selection indexes.
Genetic covariance between MY and DM
were low at the middle of the DIM but were high in
the beginning and end of DIM (Figure 6).
Accordingly, genetic correlations between for
these two traits were close to 0 around at 91 DIM
(Figure 5). Considering genetic correlation between
MY and DM different results were obtained from
previous study (Yazgan et al., 2010). While low
genetic correlations were found at the beginning, it
was high in the rest of lactation. Using different
cattle breeds between two researches may cause
thise differences.
In this research estimated genetic
correlation between MY and F were moderate and
vary from 0.30 – 0.59 until 61 DIM. After this point
it was tend to fall and very close to 0 at 196 DIM.
Results from this study generally lower when
compared with results of studies conducted by
Silvestre et al. (2005), Stoop et al. (2007), Miglior
et al. (2007), Hammami et al. (2008) and HosseinZadeh and Ardalan (2011). They found genetic
correlation between MY- F and vary from 0.43 to
0.93. It could be explained by fact that they used
Holstein dairy cows whereas; in this research
Brown Swiss cows were used different from other
studies.
As in shown Table 1, while MY was
decreasing after 46 th to end of DIM, DM and F
percentage disposed to increase as typical.
Contrary to expectations, decreased milk yield,
milking time remained constant. It could be
explained by the fact that increasing F percentage
in the milk towards end of TD. Because genetic
correlations between DM - F were very high almost
at all DIM (Figure 6). In other words, this
implied that F percentage more increased in MY
than other components (protein and lactose or
other solids). Additionally, as in shown in Figure 5,
genetic correlation trend MY-F and MY-MD were
similar. Increased percentage of F in milk and
309
Türk Tarım ve Doğa Bilimleri Dergisi 1(3): 301–311, 2014
other milk components could have reduced milk
flow and because of this, MD may have been fixed.
In this research estimated genetic
correlations between MY-MD were vary from -0.16
to 0.30 and generally lower when compared with
the correlation between milk yield and milking
speed estimated by Karacaören et al. (2006).
Eventually, for this data set were small and
obtained from a non-commercial experimental
farm, results could not be directly compared with
the results of field research. Nevertheless, suggest
about how could be variance components
estimates with 4 multiple-trait (4 traits at a time)
random regression model via restricted maximum
likelihood (REML) with AI-REML algorithm using
heterogeneous residual error interval. As
mentioned earlier, In Turkey there are only very
few studies on genetic correlation between milk
yield, fat and milking duration traits we chose to
analyze in this research. So, further study should
be focused on such research with large data sets in
Turkey.
Haile-Mariam, M., Bowman, P.J., Goddard, M.E.,
2001.
Genetic
and
environmental
correlations between test day somatic cell
count and milk yield traits. Livestock.
Production Science 73: 1–13.
Hammami, H., Rekik, B., Soyeurt, H., Ben Gara A.,
Gengler, N., 2008. Genetic parameters for
Tunisian Holstein Using a Test- Day Random
Regression Model. Journal of Dairy Science.
91: 2118–2116.
Hossein-Zadeh, N.G., Ardalan, M., 2011. Estimation
of genetic parameters for milk urea
nitrogen and its relationship with milk
constituents in Iranian Holsteins. Livestock
Science. 135: 274–281.
Ilahi, H., Kadarmideen, H.N., 2004. Bayesian
segregation analysis of milk flow in Swiss
dairy cattle using Gibbs sampling. Genetics
Selection Evolution. 36:563–576.
INTERBULL. International bull evaluation service
[Internet]. c1997-2012. Sweden: permanent
sub-committee of the International
Committee for Animal Recording (ICAR);
[cited 2007 Nov 30]. Available from:
http://www
interbull.slu.se/eval/aug07.html.
Karacaören, B., Jaffrézic, F., Kadarmideen, H.N.,
2006. Genetic parameters for functional
traits in dairy cattle from daily random
regression models. Journal of Dairy Science.
89:791–798.
Kumlu, S., 2003. Hayvan Islahı 2. Basım [Animal
Breeding 2th edition]. Ankara: Türkiye
Damızlık Sığır Yetiştiricileri Merkez Birliği
Yayınları.
Macciotta, N.P.P., Vicario, D., Cappio-Borlino, A.,
2005. Detection of different shapes of
lactation curve for milk yield in dairy cattle
by empirical mathematical models. Journal
of Dairy Science. 88:1178-1191.
Macciotta, N.P.P., Dimauro, C, Rassu, S.P.G., Steri,
R., Pulina, G., 2011. The mathematical
description of lactation curves in dairy
cattle. Ital. Journal of Animal Science. 10: 51
213-223.
Meyer, K., 2007. Wombat: a tool for mixed model
analyses in quantitative genetics by REML.
[Internet]. Version 19-05-2012. Armidale:
University of New England Australia.
Available
from
:
http://didgeridoo.une.edu.au/km/homepag
e.php.
Miglior, F., Sewalem, A., Jamrozik, J., Bohmanova,
J., Lefebvre, D.M, Moore, R.K., 2007.
Genetic analysis of milk urea nitrogen and
lactose and their relationships with other
production traits in Canadian Holstein
References
Boettcher, P.J., Dekkers J.C.M., Kolstad B.W., 1998.
Development of an udder health index for
sire selection based on somatic cell score,
udder conformation, and milking speed.
Journal of Dairy Science. 81:1157–1168.
Bohmanova, J., Miglior F., Jamrozik J., Misztal I.,
Sullivan, P.G., 2008. Comparison of Random
Regression
Models
with
Legendre
Polynomials and Linear Splines for
Production Traits and Somatic Cell Score of
Canadian Holstein Cows. Journal of Dairy
Science. 91:3627–3638.
Cobuci, J.A., Euclydes R.F., Lopes, P.S. Claudio, N.C.,
Robledo, A.T., Carmen S.P., 2005.
Estimation of genetic parameters for testday milk yield in Holstein cows using a
random regression model. Genetics and
Molecular Biology. 28: 75-83.
Duru, S., Kumlu, S., Tuncel, E., 2012. Estimation of
variance
components
and
genetic
parameters for type traits and milk yield in
Holstein cattle. Turk. J. Vet. Anim. Sci. 36(6):
585-591.
Ertuğrul, O, Orman M.N., Güneren G., 2002. Some
genetic parameters of milk production in
the Holstein breed. Turk. J. Ve.t Anim. Sci.
26: 463-469.
Galiç, A., Kumlu, S., 2012. Application of a random
regression model to estimation of genetic
parameters of test day milk yields of Turkish
Holstein Firesians. Kafkas Univ Vet Fak Derg.
18 (5): 719-724.
310
Türk Tarım ve Doğa Bilimleri Dergisi 1(3): 301–311, 2014
cattle. Journal of Dairy Science. 90, 2468–
2479.
Misztal, I., 2008. Reliable computing in estimation
of variance components. Journal of Animal
Breeding and Genetics. 125: 363-370.
Sharaby, M.A., Burnside, E.B, Hacker, R.R., 1977.
Accuracy of an automated technique for
determining individual milking rates under
field conditions. Journal of Dairy Science.
60:133.
Silvestre, A.M., Petim-Batista, F., Colaco, J., 2005.
Genetic parameter estimates of Portuguese
dairy cows for milk, fat, and protein using a
spline test-day model. Journal of Dairy
Science. 88:1225-1230.
Stoop, W.M, Bovenhuis, H., van Arendok, J.A.M.,
2007. Genetic parameters for milk urea
nitrogen in relation to milk production
traits. J. Dairy Sci. 90: 1981–1986.
Takma, Ç., Akbaş, Y., 2009. Variance components
and genetic parameter estimates using
random regression models on test day milk
yields of Holstein Friesians. The Journal of
the Faculty of Veterinary Medicine,
University of Kafkas. 15 (4): 547-551.
Tilki, M., Saatcı., M., Çolak, M., 2008. Genetic
parameters for direct and maternal effects
and estimation of breeding values for birth
weight in brown Swiss cattle. Turk. J. Vet.
Anim. Sci. 32(4): 287-292.
Ünalan, A., Cebeci, Z., 2004. Estimation of genetic
parameters and correlations for the first
three lactation milk yields in Holstein
Friesian cattle by the REML method. Turk. J.
Vet. Anim. Sci. 28: 1043-1049.
Yazgan, K., Makulska, J., Węglarz, A., Ptak, E.,
Gierdziewicz, M., 2010. Genetic relationship
between milk dry matter and other milk
traits in extended lactations of Polish
Holstein cows. Czech Journal of Animal
Science, 55 (3), 91–104.
Zwald, N.R., Weigel K.A., Chang Y.M., Welper, R.D,
Clay, J.S., 2005. Genetic evaluation of dairy
sires
for
milking
duration
using
electronically recorded milking times of
their daughters. Journal of Dairy Science.
88:1192–1198.
311